Model for arm movements during myoclonic seizures.

A model is formulated for arm movements during myoclonic (epileptic) seizures. The system described in the model, consists of a mechanical and an electrophysiological part. The model output is compared to real patient accelerometry (ACM)-data from six epilepsy patients. Eight out of ten myoclonic seizures have a good fit to the model. The values of the model parameters tuned to the real seizures are physiologically feasible. Using mean parameter values leads to agreeable fits in six out of ten myoclonic seizures. Two of the four parameters seem to be robust for variation in patient and seizure. The presented model approach leads to a better understanding of patterns in ACM-recordings that are associated with myoclonic seizures and in the future can contribute to automated detection of these patterns.